Briefings in Bioinformatics

Article	Citations
oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data	613
NetCoMi: network construction and comparison for microbiome data in R	233
BioGPT: generative pre-trained transformer for biomedical text generation and mining	223
Predicting drug–disease associations through layer attention graph convolutional network	207
LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files	198
Expression profile of immune checkpoint genes and their roles in predicting immunotherapy response	165
CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice	161
MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm	160
Multimodal deep learning for biomedical data fusion: a review	150
AntiCP 2.0: an updated model for predicting anticancer peptides	150
Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities	148
AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes	144
A deep learning method for predicting metabolite–disease associations via graph neural network	143
InstaDock: A single-click graphical user interface for molecular docking-based virtual high-throughput screening	140
Predicting the potential human lncRNA–miRNA interactions based on graph convolution network with conditional random field	136
Prognosis and personalized treatment prediction in TP53-mutant hepatocellular carcinoma: an in silico strategy towards precision oncology	130
DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence	119
Exploration of natural compounds with anti-SARS-CoV-2 activityviainhibition of SARS-CoV-2 Mpro	117
A review on drug repurposing applicable to COVID-19	116
Biological network analysis with deep learning	115
A roadmap for multi-omics data integration using deep learning	114
Circular RNAs and complex diseases: from experimental results to computational models	111
Deep-belief network for predicting potential miRNA-disease associations	110
The miRNA: a small but powerful RNA for COVID-19	108
Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source	107

Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research	104
A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information	103
GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels	103
Hiplot: a comprehensive and easy-to-use web service for boosting publication-ready biomedical data visualization	102
Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions	101
Utilizing graph machine learning within drug discovery and development	101
A comprehensive survey of regulatory network inference methods using single cell RNA sequencing data	100
SSI–DDI: substructure–substructure interactions for drug–drug interaction prediction	100
Venn diagrams in bioinformatics	99
A survey on computational models for predicting protein–protein interactions	98
Network Pharmacology and bioinformatics analyses identify intersection genes of niacin and COVID-19 as potential therapeutic targets	97
An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction	96
Systemic effects of missense mutations on SARS-CoV-2 spike glycoprotein stability and receptor-binding affinity	93
Tumor immune microenvironment lncRNAs	91
Drug repositioning based on the heterogeneous information fusion graph convolutional network	90
Molecular characterization, biological function, tumor microenvironment association and clinical significance of m6A regulators in lung adenocarcinoma	89
Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification	88
ggmsa: a visual exploration tool for multiple sequence alignment and associated data	87
Anticancer peptides prediction with deep representation learning features	86
Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19	86
Meta-i6mA: an interspecies predictor for identifying DNAN6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework	86
StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides	85
Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method	82
Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 gliob	81
Multi-view Multichannel Attention Graph Convolutional Network for miRNA–disease association prediction	81
DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites	81
Graph representation learning in bioinformatics: trends, methods and applications	81
POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability	80
A weighted bilinear neural collaborative filtering approach for drug repositioning	79
Pan-cancer analysis of NLRP3 inflammasome with potential implications in prognosis and immunotherapy in human cancer	78
Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets	77
Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework	74
Virtual screening and molecular dynamics simulation study of plant-derived compounds to identify potential inhibitors of main protease from SARS-CoV-2	74
PharmKG: a dedicated knowledge graph benchmark for bomedical data mining	74
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations	73
Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19	73
ToxinPred2: an improved method for predicting toxicity of proteins	72
Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides	71
Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19	71
MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction	71
Text mining approaches for dealing with the rapidly expanding literature on COVID-19	71
Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview	70
DeepDTAF: a deep learning method to predict protein–ligand binding affinity	69
A graph auto-encoder model for miRNA-disease associations prediction	69
Network-based modeling of herb combinations in traditional Chinese medicine	68
Semantic similarity and machine learning with ontologies	68
Predicting metabolite–disease associations based on auto-encoder and non-negative matrix factorization	67
Identification of miRNA–disease associations via deep forest ensemble learning based on autoencoder	67
Interpretation of deep learning in genomics and epigenomics	67
Clinical significance and immunogenomic landscape analyses of the immune cell signature based prognostic model for patients with breast cancer	67

MDF-SA-DDI: predicting drug–drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism	67
Artificial intelligence in drug discovery: applications and techniques	66
Molecular design in drug discovery: a comprehensive review of deep generative models	66
HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks	66
Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset	65
A survey on deep learning in DNA/RNA motif mining	64
An effective self-supervised framework for learning expressive molecular global representations to drug discovery	64
An in silico approach to identification, categorization and prediction of nucleic acid binding proteins	64
Identifying the natural polyphenol catechin as a multi-targeted agent against SARS-CoV-2 for the plausible therapy of COVID-19: an integrated computational approach	63
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data	63
Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data	62
Network-based identification genetic effect of SARS-CoV-2 infections to Idiopathic pulmonary fibrosis (IPF) patients	61
DeepYY1: a deep learning approach to identify YY1-mediated chromatin loops	61
An approach for normalization and quality control for NanoString RNA expression data	61
NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning	60
Computational drug repositioning based on multi-similarities bilinear matrix factorization	60
A protocol for dynamic model calibration	59
Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison	59
Pharmacoinformatics and molecular dynamics simulation-based phytochemical screening of neem plant (Azadiractha indica) against human cancer by targeting MCM7 protein	59
Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients	58
Learning spatial structures of proteins improves protein–protein interaction prediction	58
Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design	57
ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation	57
Deep learning meets metabolomics: a methodological perspective	57
m6A regulator-mediated methylation modification patterns and characteristics of immunity and stemness in low-grade glioma	56
Deep drug-target binding affinity prediction with multiple attention blocks	56
GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest	56
FitDock: protein–ligand docking by template fitting	56
DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity	55
AlphaFold2-aware protein–DNA binding site prediction using graph transformer	55
A novel antibacterial peptide recognition algorithm based on BERT	55
A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2	55
Machine learning meets omics: applications and perspectives	55
Benchmarking variant callers in next-generation and third-generation sequencing analysis	54
A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: an in silico investigation	54
Prediction and collection of protein–metabolite interactions	53
DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method	53
Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec	53
Improving cancer driver gene identification using multi-task learning on graph convolutional network	53
A simple guide to de novo transcriptome assembly and annotation	53
Comprehensive investigation of pathway enrichment methods for functional interpretation of LC–MS global metabolomics data	53
Exploring associations of non-coding RNAs in human diseases via three-matrix factorization with hypergraph-regular terms on center kernel alignment	53
Machine learning approach to gene essentiality prediction: a review	52
Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding	52
Comparative analysis of molecular fingerprints in prediction of drug combination effects	52
Cell–cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies	52
A heterogeneous network embedding framework for predicting similarity-based drug-target interactions	52
ATSE: a peptide toxicity predictor by exploiting structural and evolutionary information based on graph neural network and attention mechanism	52
CAMOIP: a web server for comprehensive analysis on multi-omics of immunotherapy in pan-cancer	52
DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor	52
A review on longitudinal data analysis with random forest	52
Attention-based Knowledge Graph Representation Learning for Predicting Drug-drug Interactions	51
Large-scale benchmark study of survival prediction methods using multi-omics data	51
Using deep neural networks and biological subwords to detect protein S-sulfenylation sites	50
Pharmacometabonomics: data processing and statistical analysis	50
Drug–drug interaction prediction with learnable size-adaptive molecular substructures	50
Integrative pharmacological mechanism of vitamin C combined with glycyrrhizic acid against COVID-19: findings of bioinformatics analyses	50
Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models	50
Evaluating the state of the art in missing data imputation for clinical data	50
Machine learning methods, databases and tools for drug combination prediction	50
Deep-DRM: a computational method for identifying disease-related metabolites based on graph deep learning approaches	50
HVIDB: a comprehensive database for human–virus protein–protein interactions	50
Ferroptosis-related lncRNA pairs to predict the clinical outcome and molecular characteristics of pancreatic ductal adenocarcinoma	50
Improving protein–ligand docking and screening accuracies by incorporating a scoring function correction term	50
FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction	49
A comprehensive overview and critical evaluation of gene regulatory network inference technologies	49
Predicting potential small molecule–miRNA associations based on bounded nuclear norm regularization	49
Tensor decomposition with relational constraints for predicting multiple types of microRNA-disease associations	49
FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network	49
Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors	49
MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph	49
Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma	49
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction	49
Deep learning in retrosynthesis planning: datasets, models and tools	48
A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells	48
LSTM-PHV: prediction of human-virus protein–protein interactions by LSTM with word2vec	48
DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach	48
Accurate prediction of inter-protein residue–residue contacts for homo-oligomeric protein complexes	47
ConSIG: consistent discovery of molecular signature from OMIC data	47
STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction	47

ENNAVIA is a novel method which employs neural networks for antiviral and anti-coronavirus activity prediction for therapeutic peptides	47
iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network	47
Accurate protein function prediction via graph attention networks with predicted structure information	47
Accurate and fast cell marker gene identification with COSG	47
SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes	46
Multi-omics approaches for revealing the complexity of cardiovascular disease	46
Recent advances in network-based methods for disease gene prediction	46
Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification	45
Transcriptional landscape of cholangiocarcinoma revealed by weighted gene coexpression network analysis	45
iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types	45
Unsupervised and self-supervised deep learning approaches for biomedical text mining	44
Predicting human microbe–disease associations via graph attention networks with inductive matrix completion	43
Protein design via deep learning	43
fastDRH: a webserver to predict and analyze protein–ligand complexes based on molecular docking and MM/PB(GB)SA computation	43
NPI-GNN: Predicting ncRNA–protein interactions with deep graph neural networks	43
PhaTYP: predicting the lifestyle for bacteriophages using BERT	43
DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences	43
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data	42
Identification of drug–target interactions via multiple kernel-based triple collaborative matrix factorization	42
AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification	42
scCancer: a package for automated processing of single-cell RNA-seq data in cancer	42
Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks	42
Transcriptome analysis of cepharanthine against a SARS-CoV-2-related coronavirus	42
Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks	42
AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug–drug interaction prediction	42
Drug–target interaction predication via multi-channel graph neural networks	41
Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes	41
PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques	41
Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery	41
Recent advances in user-friendly computational tools to engineer protein function	41
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective	41
DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding	40
Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion	40
Protein–RNA interaction prediction with deep learning: structure matters	40
Computational resources for identifying and describing proteins driving liquid–liquid phase separation	40
Comprehensive assessment of cellular senescence in the tumor microenvironment	40
Identification and characterization of circRNAs encoded by MERS-CoV, SARS-CoV-1 and SARS-CoV-2	40
Integrative machine learning framework for the identification of cell-specific enhancers from the human genome	40
Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology	39
Porpoise: a new approach for accurate prediction of RNA pseudouridine sites	39
Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules	39
Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine	39
Drug–drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings	39
FireProtASR: A Web Server for Fully Automated Ancestral Sequence Reconstruction	39
AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches	39
GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field	39
Immune infiltration and clinical significance analyses of the coagulation-related genes in hepatocellular carcinoma	39
A cross-study analysis of drug response prediction in cancer cell lines	39
Computational methods for the integrative analysis of single-cell data	39
GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction	38
Bioinformatics resources for SARS-CoV-2 discovery and surveillance	38
KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network	38
Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing	38
Machine learning for synergistic network pharmacology: a comprehensive overview	38
A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma	38
Predicting miRNA–disease associations via learning multimodal networks and fusing mixed neighborhood information	38
Integrated unsupervised–supervised modeling and prediction of protein–peptide affinities at structural level	38
Attention is all you need: utilizing attention in AI-enabled drug discovery	38
RNA–RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection	38
Bioinformatics and system biology approach to identify the influences of COVID-19 on cardiovascular and hypertensive comorbidities	38
Critical downstream analysis steps for single-cell RNA sequencing data	37
Topoly: Python package to analyze topology of polymers	37
A network embedding framework based on integrating multiplex network for drug combination prediction	37
Modeling and analyzing single-cell multimodal data with deep parametric inference	37
A comprehensive survey on computational methods of non-coding RNA and disease association prediction	37
RNMFLP: Predicting circRNA–disease associations based on robust nonnegative matrix factorization and label propagation	37
BioRED: a rich biomedical relation extraction dataset	37
Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models	37
Cloud 3D-QSAR: a web tool for the development of quantitative structure–activity relationship models in drug discovery	37
Predicting drug–drug interactions by graph convolutional network with multi-kernel	37
DeepFeature: feature selection in nonimage data using convolutional neural network	36
Identifying multi-functional bioactive peptide functions using multi-label deep learning	36
Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies	36
MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors	36
AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom	36
Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine	36
ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm	35
epitope3D: a machine learning method for conformational B-cell epitope prediction	35
Predicting enhancer-promoter interactions by deep learning and matching heuristic	35
Is acupuncture effective in the treatment of COVID-19 related symptoms? Based on bioinformatics/network topology strategy	35
NmRF: identification of multispecies RNA 2’-O-methylation modification sites from RNA sequences	35
Heterogeneous graph attention network based on meta-paths for lncRNA–disease association prediction	35
Diseasome and comorbidities complexities of SARS-CoV-2 infection with common malignant diseases	35
Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data	35
Prediction of RNA secondary structure including pseudoknots for long sequences	34
Integrated omics analysis reveals the alteration of gut microbe–metabolites in obese adults	34
iLoc-miRNA: extracellular/intracellular miRNA prediction using deep BiLSTM with attention mechanism	34
Forman persistent Ricci curvature (FPRC)-based machine learning models for protein–ligand binding affinity prediction	34
Assessing the performance of computational predictors for estimating protein stability changes upon missense mutations	34
PreTP-EL: prediction of therapeutic peptides based on ensemble learning	34
SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targets	34
Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients	34
Pathogenetic profiling of COVID-19 and SARS-like viruses	34
Accelerating bioactive peptide discovery via mutual information-based meta-learning	34
spaCI: deciphering spatial cellular communications through adaptive graph model	34